Impact of Covid-19 on the Performance of The Nigeria`s Stock Market

This study identifies the structural break date in the series of All Share Index (ASI) of the Nigeria’s capital market using innovational outlier methodology with the Augmented Dickey-Fuller unit root with structural break test. The study also examines the descriptive characteristics and model structure of ASI before and after the identified break date using ARIMA methodology. It uses daily data of ASI from November 27, 2018 to November 24, 2020. The results indicate that the break date is March 6, 2020. The mean results decreased after the break. The series before the break follows ARIMA (3,1,12), while it follows ARIMA (7,1,9) after the break. The diagnostic test revealed that the ARIMA (7,1,9) fails to capture the entire variation in the series. The modified model for post break period is AR(7), MA(8) and MA(9) process. However, the estimated volatility of the series decreased after the break. The study recommends that capital market studies and policies going forward should incorporate the impact of Covid-19 induced structural break.

The effect of the introduction of market makers on stock liquidity: evidence from the Brazilian stock market

Purpose: This study aimed to analyze whether the introduction of market makers as specialized intermediaries in the trading of stocks listed on the Brazilian stock exchange is a useful procedure for increasing the market liquidity of these assets. Methodology: The Chow structural break test was performed in the time series of the liquidity proxies, average spread, turnover ratio, and financial volume on a sample of 55 stocks. We chose to consider data in the window of 260 days before and after the start of the market maker's activity, because it represents the approximate number of trading sessions in a year, and to avoid erroneous conclusions due to the volatility of the Brazilian stock market. Results: The results showed with a 99% confidence level that after the introduction of market makers, (i) 67% of the stocks analyzed had abrupt and statistically significant changes in the average spread; (ii) 47% in the turnover ratio; and (iii) 60% had changes in the volume transactions. At the confidence level of 95%, (i) 76% of the stocks analyzed showed abrupt changes in the average spread; (ii) 65% had changes in turnover; (iii) and 69% had changes in the trading volume. Using a lower confidence level of 90%, the results revealed 85% of the stocks had abrupt and statistically significant changes in the average spread, 78% in the turnover ratio, and 73% in the trading volume. Contributions of the Study: This paper provides strong evidence on the performance of market makers and the influence they have on the market liquidity of stocks traded on the Brazilian stock exchange. We found that contracting market makers increase market liquidity and contribute significantly to the assets’ transactions.

Quantifying the data-dredging bias in structural break tests

Structural break tests are often applied as a pre-step to ensure the validity of subsequent statistical analyses. Without any a priori knowledge of the type of breaks to expect, eye-balling the data can indicate changes in some parameter, e.g., the mean. This, however, can distort the result of a structural break test for that parameter, because the data themselves suggested the hypothesis. In this paper, we formalize the eye-balling procedure and theoretically derive the implied size distortion of the structural break test. We also show that eye-balling a stretch of historical data for possible changes in a parameter does not invalidate the subsequent procedure that monitors for structural change in new incoming observations. An empirical application to Bitcoin returns shows that taking into account the data-dredging bias, which is incurred by looking at the data, can lead to different test decisions.

The time-varying effects of oil prices on oil–gas stock returns of the fragile five countries

This study analyzes oil price exposure of the oil–gas sector stock returns for the fragile five countries based on a multi-factor asset pricing model using daily data from 29 May 1996 to 27 January 2020. The endogenous structural break test suggests the presence of serious parameter instabilities due to fluctuations in the oil and stock markets over the period under study. Moreover, the time-varying estimates indicate that the oil–gas sectors of these countries are riskier than the overall stock market. The results further suggest that, except for Indonesia, oil prices have a positive impact on the sectoral returns of all markets, whereas the impact of the exchange rates on the oil–gas sector returns varies across time and countries.

Energy Consumption and Foreign Direct Investment in Nigeria: A Structural Break Analysis

The study analyses the structural break impact on the relationship between energy consumption and foreign direct investment in Nigeria from 1970 to 2015. The study accounts for the structural break and estimates the short-run and long-run relationship between energy consumption and foreign direct investment using ARDL estimation technique and Bai-Perron Least Squares Break Point. It was observed from the findings that a strong long-run cointegrating relationship exist between energy consumption and foreign direct investment with and without structural break. The structural break test reveals a break period of 1995 which supports the occurrence of oil price review by OPEC in 1995. Also, the ARDL estimate result revealed that energy consumption, trade and exchange rate adversely attract foreign direct investment, while GDP positively attract foreign direct investment both in the short-run and long-run in Nigeria. The study concludes that even though Nigeria’s GDP is trending towards attracting FDI into the economy, energy consumption, trade and exchange rate obstruct the attraction through the additional cost incurred as a result of imbalances in the variables. A major recommendation from the findings is that energy policies need quick re-visit in Nigeria. However, they will - due to the pressure exerted by the constant growth of the population, i.e. on the demand side, demand inflation will constantly manifest.

Impact of gender and education on corporate social responsibility: evidence from Taiwan

This paper empirically studies the impact of female proportion and the background of the board on corporate social responsibility (CSR) disclosure of Taiwanese listed firms. The different groups of board size are detected by the structural break test, which is used as the threshold for dividing subsamples. The results show that the higher proportion of women and accounting background of board of directors, the more CSR disclosure for firms with more than 11 directors in the board, implying that women and accounting background directors can only promote their compassionate and reciprocal in CSR decision-making in large board firms. Overall, the empirical results poorly support the efficiency hypothesis suggesting that the board of directors is more powerful when it has high gender diversity. This study also confirms that the linear regression method may not be able to fully present the various possible relationships between the variables.

The curvilinear relationship between environmental pollution and economic growth

Purpose The genesis of Environmental Kuznets curve (EKC) of “grow now clean later” has led to a substantial deterioration of local as well as the global environment. India has not been spared of this malaise and accounts for the third-largest carbon dioxide emitter in the world. Thus, the present study revisits the curvilinear relationship between economic growth and environmental pollution in case of India over the period of 1971-2014. Design/methodology/approach Dickey–Fuller generalised least square (DF-GLS) test developed by Elliott et al. is used to ensure that none of the variables is I(2). The study applies the autoregressive distributed lag (ARDL) bounds estimation technique to test for the existence of cointegration among variables and estimate long-run and short-run parameters. The study also applies the Bai–Perron structural break test with unknown break date to determine the threshold point. The study further uses the vector error correction model (VECM) Granger causality test to check the direction of causality between variables. Findings The ARDL bounds estimation technique confirms the cointegration among variables. The long-run coefficients of energy consumption, economic growth and financial development are found to have an adverse impact on environmental quality. The results also validate the existence of conventional EKC hypothesis. Bai–Perron structural break test, along with t-test and scatter graph, shows that inverted U-shaped relationship between environmental pollution and economic growth holds true. The VECM-based causality results support “growth hypothesis” both in the long run and short run. Research limitations/implications This study refrained from considering a variety of variables, as the main intention of the study is to investigate whether any threshold or turnaround point exists for India. The future studies should consider a new set of variables (e.g. population, corruption index, social indicators, political scenario, energy research and development expenditures, foreign capital inflows, public investment towards alternate energy exploration, etc.) in the estimation of EKC hypothesis. Practical implications The results validate the existence of conventional EKC hypothesis. Thereby the study argues that instead of being a threat to environmental quality, economic growth is observed to generate a sustainable environment to live in. Further, bi-directional causality is found between carbon emissions and economic growth. Thus, any effort to mitigate CO2 or environment conservation policy will impede economic growth. Consequently, controlling primary energy consumption and supply and replacing it with renewable and clean energy could be desirable for climate change mitigation. Originality/value The data set has been refined so that the EKC estimation issues raised by Stern (2004) are addressed. In particular, statistical properties of the data set such as serial correlation, presence of a stochastic or deterministic trend, has been adequately taken care of to remove any spurious correlation. Finally, various control variables have been included to provide consideration to issues of model adequacy, such as the possibility of omitted variables bias. To the authors’ best knowledge, there is no India-specific study which has taken care of data-related issues, as suggested by Stern, in the estimation of a curvilinear relationship between environmental degradation and economic growth in India. Further, this is the first study which has used Bai–Perron structural break test with unknown break date to identify the threshold point while estimating EKC in India.

Causality Relationship between Central Bank Reforms and Inflation: Evidence from Developing Countries

This study provides evidence on the relationship between central bank reforms and inflation dynamics in a sample of 37 developing countries. We use panel structural break test and Granger non‐causality tests on annual inflation and the legal index of central bank independence (CBI), as a proxy of central bank reform, over 40 years period. The empirical results indicate a positive effect of central bank independence on inflation stabilization. Besides, we find that there exists bi-directional causality between central bank reforms and inflation. These findings suggest that central bank independence is beneficial in terms of sustained macroeconomic stabilization and should harness among developing countries. In particular, reforms should design to give central banks more autonomy in the conduct of monetary policy and financial sector regulation. JEL Classifications: E31, E58How to Cite:Anwar, C. J., & Nicholas, O. (2020). Causality Relationship Between Central Bank Reforms and Inflation: Evidence from Developing Countries. Signifikan: Jurnal Ilmu Ekonomi, Vol. 9(1), 15-30. doi: http://dx.doi.org/10.15408/sjie.v9i1.10955.

A new structural break test for panels with common factors

Summary This paper develops new tests against a structural break in panel data models with common factors when T is fixed, where T denotes the number of observations over time. For this class of models, the available tests against a structural break are valid only under the assumption that T is ‘large’. However, this may be a stringent requirement—more commonly so in datasets with annual time frequency, in which case the sample may cover a relatively long period even if T is not large. The proposed approach builds upon existing generalized method of moments methodology and develops Distance-type and Lagrange Multiplier-type tests for detecting a structural break, both when the break point is known and when it is unknown. The proposed methodology permits weak exogeneity and/or endogeneity of the regressors. In a simulation study, the method performed well, in terms of size and power, as well as in terms of successfully locating the time of the structural break. The method is illustrated by testing the so-called ‘Gibrat’s Law’, using a dataset from 4,128 financial institutions, each one observed for the period 2002–2014.